Convergence of Learning Algorithms without a Projection Facility

Seppo Honkapohja and George Evans

No 109, CESifo Working Paper Series from CESifo

Abstract: Drawing upon recent contributions in the statistical literature, we present new results on the convergence of recursive, stochastic algorithms which can be applied to eonomic models with learning and which generalize previous results. The formal results provide probability bounds for convergence which can be used to describe the local stability under learning of rational expectations equilibria in stochastic models. Economic examples include local stability in a multivariate linear model with multiple equilibria and global convergence in a model with a unique equilibrium.

Date: 1996
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.cesifo.org/DocDL/ces_wp109.pdf (application/pdf)

Related works:
Journal Article: Convergence of learning algorithms without a projection facility (1998)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_109

Access Statistics for this paper

More papers in CESifo Working Paper Series from CESifo Contact information at EDIRC.
Bibliographic data for series maintained by Klaus Wohlrabe ().